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231225s2024 xx |||||o 00| ||eng c |
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|a 10.1080/09593330.2022.2034978
|2 doi
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|a eng
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| 100 |
1 |
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|a AlZubi, Ahmad Ali
|e verfasserin
|4 aut
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1 |
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|a IoT-based automated water pollution treatment using machine learning classifiers
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|c 2024
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| 336 |
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 19.04.2024
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|a Date Revised 19.04.2024
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Water is one of the most vital sources for the survival of life. In the globe, the accessibility of water in safe and healthy ways is a major concern. The consumption of unsafe water may lead to health risks. Therefore, it is necessary to classify and monitor the quality of water, but the main issue is that sufficient parametric quality measures are not available with advanced technology. To overcome the above issue, this paper presents an IoT-based automated water quality monitoring system using cloud and machine learning algorithms. It contains various sensor devices such as pH sensors, temperature sensors, turbidity sensors, and conductivity sensors. The classification of water quality in an accurate way is achieved by using the fusion of K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). The sensor values are generated and transferred in the cloud server via Node MCU with low power wide area networks (LPWAN). This proposed work can replace the classification and monitoring of the traditional method to qualify the water status. It helps to save human beings from various infections and diseases caused by the unsafe usage of water. Water quality classification is very important to create an eco-friendly environment. This proposed machine learning algorithm KNN + SVM is tested by 10-fold cross-validation and the highest accuracy is 0.94, when compared with the existing algorithm
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| 650 |
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|a Journal Article
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| 650 |
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|a KNN
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| 650 |
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|a LPWAN
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| 650 |
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|a Node MCU
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| 650 |
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|a SVM
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| 650 |
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|a sensors
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| 773 |
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|i Enthalten in
|t Environmental technology
|d 1993
|g 45(2024), 12 vom: 01. Apr., Seite 2299-2307
|w (DE-627)NLM098202545
|x 1479-487X
|7 nnns
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| 773 |
1 |
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|g volume:45
|g year:2024
|g number:12
|g day:01
|g month:04
|g pages:2299-2307
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| 856 |
4 |
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|u http://dx.doi.org/10.1080/09593330.2022.2034978
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